Add pyvespa notebooks

README.md

@@ -6,6 +6,7 @@
   <img alt="#Vespa" width="200" src="https://assets.vespa.ai/logos/Vespa-logo-dark-RGB.svg" style="margin-bottom: 25px;">
 </picture>
 
+
 # Vespa sample applications
 First-time users should go through the [getting-started](https://docs.vespa.ai/en/getting-started.html) guides first.
 
@@ -16,62 +17,132 @@ just follow the same steps as for [vector-search](#vector-search), adding securi
 For operational sample applications, see [examples/operations](examples/operations). See
 also [PyVespa examples](https://pyvespa.readthedocs.io/en/latest/examples.html). 
 
+
+
 ## Getting started - Basic Sample Applications
 
+
 ### Basic album-recommendation
 The [album-recommendation](album-recommendation/) is the intro application to Vespa.
 Learn how to configure the schema for simple recommendation and search use cases.
 
+
 ### Vector Search
 There is a growing interest in AI-powered vector representations of unstructured multimodal data
 and searching efficiently over these representations.
 [vector-search](https://github.com/vespa-cloud/vector-search)
 describes how your organization can unlock the full potential of multimodal AI-powered vector representations
 using Vespa Cloud - the industry-leading managed Vector Search Service.
 
+
 ### Simple hybrid semantic search
 The [simple semantic search](simple-semantic-search/)
 application demonstrates indexed vector search using `HNSW`, 
 creating embedding vectors from a transformer language model inside Vespa, and hybrid
 text and semantic ranking. This app also demonstrates using native Vespa embedders. 
 
+
 ### Retrieval Augmented Generation (RAG)
 The [retrieval-augmented-generation](retrieval-augmented-generation) sample application
 demonstrates how to build an end-to-end RAG pipeline with API-based and local LLMs.  
 
+
 ### Indexing multiple vectors per field
 The [Vespa Multi-Vector Indexing with HNSW](multi-vector-indexing/) app demonstrates how to 
 index multiple vectors per document field for semantic search for longer documents.  
 
+
 ### Vespa streaming mode for naturally partitioned data
 The [vector-streaming-search](vector-streaming-search) app 
 demonstrates how to use vector streaming search. See also [blog post](https://blog.vespa.ai/announcing-vector-streaming-search/). 
 
+
 ### ColBERT token-level embeddings
 The [colbert](colbert) application demonstrates how to 
 use the Vespa [colbert-embedder](https://docs.vespa.ai/en/embedding.html#colbert-embedder) 
 for explainable semantic search with better accuracy than regular
 text embedding models. 
 
+
 ### ColBERT token-level embeddings for long documents
 The [colbert-long](colbert-long) application demonstrates how to 
 use the Vespa [colbert-embedder](https://docs.vespa.ai/en/embedding.html#colbert-embedder) 
 for explainable semantic search for longer documents. 
 
+
 ### SPLADE sparse learned weights for ranking
 The [splade](splade) application demonstrates how to 
 use the Vespa [splade-embedder](https://docs.vespa.ai/en/embedding.html#splade-embedder) for 
 semantic search using sparse vector representations. 
 
+
 ### Multilingual semantic search
 The [multilingual](multilingual-search) sample application demonstrates multilingual semantic search 
 with multilingual text embedding models. 
 
+
 ### Customizing embeddings 
 The [custom-embeddings](custom-embeddings) application demonstrates customizing frozen document embeddings for downstream tasks. 
 
+
+### Pyvespa intro notebooks
+* [Querying Vespa](https://pyvespa.readthedocs.io/en/latest/query.html)
+* [Read and write operations](https://pyvespa.readthedocs.io/en/latest/reads-writes.html)
+* [Hybrid Search - Quickstart](https://pyvespa.readthedocs.io/en/latest/getting-started-pyvespa.html)
+* [Hybrid Search - Quickstart on Vespa Cloud](https://pyvespa.readthedocs.io/en/latest/getting-started-pyvespa-cloud.html)
+* [Application packages](https://pyvespa.readthedocs.io/en/latest/application-packages.html)
+* [Advanced Configuration](https://pyvespa.readthedocs.io/en/latest/advanced-configuration.html)
+* [Authenticating to Vespa Cloud](https://pyvespa.readthedocs.io/en/latest/authenticating-to-vespa-cloud.html)
+* [Pyvespa examples](https://pyvespa.readthedocs.io/en/latest/examples/pyvespa-examples.html)
+
+
+
+## Ranking
+* [Using Mixedbread.ai cross-encoder for reranking in Vespa.ai](https://pyvespa.readthedocs.io/en/latest/examples/cross-encoders-for-global-reranking.html)
+* [Standalone ColBERT + Vespa for long-context ranking](https://pyvespa.readthedocs.io/en/latest/examples/colbert_standalone_long_context_Vespa-cloud.html)
+* [Standalone ColBERT with Vespa for end-to-end retrieval and ranking](https://pyvespa.readthedocs.io/en/latest/examples/colbert_standalone_Vespa-cloud.html)
+* [LightGBM: Training the model with Vespa features](https://pyvespa.readthedocs.io/en/latest/examples/lightgbm-with-categorical.html)
+* [LightGBM: Mapping model features to Vespa features](https://pyvespa.readthedocs.io/en/latest/examples/lightgbm-with-categorical-mapping.html)
+
+
+
+## Vector Search, Hybrid Search and Embeddings
+* [Billion-scale vector search with Cohere binary embeddings in Vespa](https://pyvespa.readthedocs.io/en/latest/examples/billion-scale-vector-search-with-cohere-embeddings-cloud.html)
+* [Multi-vector indexing with HNSW](https://pyvespa.readthedocs.io/en/latest/examples/multi-vector-indexing.html)
+* [BGE-M3 - The Mother of all embedding models](https://pyvespa.readthedocs.io/en/latest/examples/mother-of-all-embedding-models-cloud.html)
+* [Evaluating retrieval with Snowflake arctic embed](https://pyvespa.readthedocs.io/en/latest/examples/evaluating-with-snowflake-arctic-embed.html)
+* [Multilingual Hybrid Search with Cohere binary embeddings and Vespa](https://pyvespa.readthedocs.io/en/latest/examples/multilingual-multi-vector-reps-with-cohere-cloud.html)
+* [Using Cohere Binary Embeddings in Vespa](https://pyvespa.readthedocs.io/en/latest/examples/cohere-binary-vectors-in-vespa-cloud.html)
+* [Exploring the potential of OpenAI Matryoshka 🪆 embeddings with Vespa](https://pyvespa.readthedocs.io/en/latest/examples/Matryoshka_embeddings_in_Vespa-cloud.html)
+* [Using Mixedbread.ai embedding model with support for binary vectors](https://pyvespa.readthedocs.io/en/latest/examples/mixedbread-binary-embeddings-with-sentence-transformers-cloud.html)
+
+
+
+## Visual search
+* [PDF-Retrieval using ColQWen2 (ColPali) with Vespa](https://pyvespa.readthedocs.io/en/latest/examples/pdf-retrieval-with-ColQwen2-vlm_Vespa-cloud.html)
+* [ColPali Ranking Experiments on DocVQA](https://pyvespa.readthedocs.io/en/latest/examples/colpali-benchmark-vqa-vlm_Vespa-cloud.html)
+* [Vespa 🤝 ColPali: Efficient Document Retrieval with Vision Language Models](https://pyvespa.readthedocs.io/en/latest/examples/colpali-document-retrieval-vision-language-models-cloud.html)
+* [Scaling ColPALI (VLM) Retrieval](https://pyvespa.readthedocs.io/en/latest/examples/simplified-retrieval-with-colpali-vlm_Vespa-cloud.html)
+
+
+
+## RAG and Generative AI
+* [Turbocharge RAG with LangChain and Vespa Streaming Mode for Partitioned Data](https://pyvespa.readthedocs.io/en/latest/examples/turbocharge-rag-with-langchain-and-vespa-streaming-mode-cloud.html)
+* [Visual PDF RAG with Vespa - ColPali demo application](https://pyvespa.readthedocs.io/en/latest/examples/visual_pdf_rag_with_vespa_colpali_cloud.html)
+* [Chat with your pdfs with ColBERT, langchain, and Vespa](https://pyvespa.readthedocs.io/en/latest/examples/chat_with_your_pdfs_using_colbert_langchain_and_Vespa-cloud.html)
+* [Building cost-efficient retrieval-augmented personal AI assistants](https://pyvespa.readthedocs.io/en/latest/examples/scaling-personal-ai-assistants-with-streaming-mode-cloud.html)
+
+
+
+## Performance
+* [Feeding performance](https://pyvespa.readthedocs.io/en/latest/examples/feed_performance.html)
+* [Feeding to Vespa Cloud](https://pyvespa.readthedocs.io/en/latest/examples/feed_performance_cloud.html)
+
+
+
 ## More advanced sample applications
 
+
 ### News search and recommendation tutorial 
 The [news](news/) sample application used in the [Vespa tutorial](https://docs.vespa.ai/en/tutorials/news-1-getting-started.html).
 This application demonstrates basic search functionality.
@@ -82,10 +153,12 @@ is used to retrieve recommended content for a user.
 This app also demonstrates using [parent-child](https://docs.vespa.ai/en/parent-child.html) 
 relationships in Vespa.
 
+
 ### Billion-scale Image Search
 This [billion-scale-image-search](billion-scale-image-search/) app demonstrates 
 billion-scale image search using CLIP retrieval. It features separation of compute from storage and query time vector similarity de-duping. PCA dimension reduction and more.
 
+
 ### State-of-the-art Text Ranking
 This [msmarco-ranking](msmarco-ranking/) application demonstrates 
 how to represent state-of-the-art text ranking using Transformer (BERT) models.
@@ -95,26 +168,29 @@ bi-encoders, cross-encoders, and late-interaction models (ColBERT).
 See also the more simplistic [text-search](text-search) app that demonstrates 
 traditional text search using BM25/Vespa nativeRank.
 
-### Next generation E-Commerce Search
 
+### Next generation E-Commerce Search
 The [use-case-shopping](use-case-shopping/) app creates an end-to-end E-Commerce shopping engine.
 This use case also bundles a frontend application.
 It uses the [Amazon product data set](http://jmcauley.ucsd.edu/data/amazon/links.html).
 It demonstrates building next generation E-commerce Search using Vespa. See
 also the [commerce-product-ranking](commerce-product-ranking/) sample application for using
 learning-to-rank techniques (Including `XGBoost` and `LightGBM`) for improving product search ranking.
 
+
 ### Search as you type and query suggestions 
 The [incremental-search](incremental-search/) application demonstrates search-as-you-type functionality, where for each keystroke of the user, it retrieves matching documents. 
 It also demonstrates search suggestions (query auto-completion).
 
+
 ### Vespa as ML inference server (model-inference)
 The [model-inference](model-inference/) application demonstrates 
 using Vespa as a stateless ML model inference server
 where Vespa takes care of distributing ML models to multiple serving containers,
 offering horizontal scaling and safe deployment.
 Model versioning and feature processing pipeline.
 
+
 ### Vespa Documentation Search
 [vespa-documentation-search](https://github.com/vespa-cloud/vespa-documentation-search)
 is the search application that powers [search.vespa.ai](https://search.vespa.ai/) -
@@ -124,6 +200,7 @@ as it has system, staging and production test examples.
 It uses the [Document API](https://docs.vespa.ai/en/document-api-guide.html)
 both for regular PUT operations but also for UPDATE with _create-if-nonexistent_.
 
+
 ### CORD-19 Search
 [cord19.vespa.ai](https://cord19.vespa.ai/) is a full-featured application,
 based on the [Covid-19 Open Research Dataset](https://huggingface.co/datasets/allenai/cord19):
@@ -155,4 +232,3 @@ Refer to the [Developer Guide](https://docs.vespa.ai/en/developer-guide.html) fo
 
 [![sample-apps verify-guides](https://api.screwdriver.cd/v4/pipelines/7038/verify-guides/badge)](https://cd.screwdriver.cd/pipelines/7038/)
 [![sample-apps verify-guides-big](https://api.screwdriver.cd/v4/pipelines/7038/verify-guides-big/badge)](https://cd.screwdriver.cd/pipelines/7038/)
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